Disclosed is a domain engineering analysis solution that determines relevance of a domain name to a brand name in which a domain name, brand name, and identification of a substring of the domain name may be provided to or obtained by a computer embodying a domain engineering analyzer. A list of features may be determined. The list of features may include a lexicon, or a set of key-value pairs that encode information about terms included as substrings in the domain name. Determining the features may include obtaining a language model for each term, analyzing a cluster of language models closest to the obtained language model, and determining and scoring a relevance of each term to the brand name. The determined relevance and score of each term may be provided to a client. This relevance analysis can be dynamically applied in an online process or proactively applied in an offline process.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A method, comprising: determining, by a computer in pre-processing, terms that are relevant to a brand, the determining including: analyzing words used in domain names registered to the brand; for each term of interest, obtaining a language model and language models that are most similar to the term of interest, the obtaining including: computing a distance between the language model for the term of interest and each of a plurality of language models associated with a set of words; and determining a cluster of least-distant language models from the plurality of language models based on the distance between the language model for the term of interest and a respective language model of the plurality of language models associated with the set of words, the cluster of least-distant language models associated with neighbors of the term of interest; computing a score indicating a relevance of the term of interest to the brand, the computing including determining a number of the neighbors appearing in the domain names registered to the brand; determining, by a computer in processing an input domain name, character counts and locations of the characters in the input domain name; for each term indicated by the character counts and the locations of the characters in the input domain name, retrieving a relevance score computed in the pre-processing; determining a final score for the input domain name based at least on the relevance score of each term in the input domain name; and providing the final score to a classifier for classifying the input domain name in relevance to the brand.
2. The method of claim 1 , wherein the processing the input domain name includes obtaining a substring of the input domain name that is at least an approximate match to the brand name.
3. The method of claim 2 , wherein the substring of the input domain name includes: an exact match to the brand name, or an approximate match that matches the brand name within a predetermined threshold measure of distance from the brand name.
4. The method of claim 2 , wherein the obtaining the substring of the input domain name that is at least an approximate match to the brand name includes: obtaining a pre-processed set of key-value pairs, wherein the pre-processed set of key-value pairs includes one or more index values indicating a location of the substring of the first domain name that is at least an approximate match to the brand name as a value of a first pre-processed key-value pair of the pre-processed set of key-value pairs, as the substring of the input domain name.
5. The method of claim 1 , further comprising: obtaining the brand name.
6. The method of claim 1 , further comprising: obtaining a pre-processed set of key-value pairs that includes the input domain name as a value of a first pre-processed key-value pair, and the brand name as a value of a second pre-processed key-value pair.
7. The method of claim 1 , wherein the obtaining includes analyzing a plurality of word vectors closest to a word vector for the term of interest.
8. The method of claim 1 , wherein the determining the duster of least-distant language models includes analyzing a plurality of language models within a predetermined threshold distance from the language model for the term of interest.
9. A system, comprising: a processor; a non-transitory computer-readable medium; and stored instructions translatable by the processor for: determining, in pre-processing, terms that are relevant to a brand, the determining including: analyzing words used in domain names registered to the brand; for each term of interest, obtaining a language model and language models that are most similar to the term of interest, the obtaining including: computing a distance between the language model for the term of interest and each of a plurality of language models associated with a set of words; and determining a cluster of least-distant language models from the plurality of language models based on the distance between the language model for the term of interest and a respective language model of the plurality of language models associated with the set of words, the cluster of least-distant language models associated with neighbors of the term of interest; computing a score indicating a relevance of the term of interest to the brand, the computing including determining a number of the neighbors appearing in the domain names registered to the brand; determining, in processing an input domain name, character counts and locations of the characters in the input domain name; for each term indicated by the character counts and the locations of the characters in the input domain name, retrieving a relevance score computed in the pre-processing; determining a final score for the input domain name based at least on the relevance score of each term in the input domain name; and providing the final score to a classifier for classifying the input domain name in relevance to the brand.
10. The system of claim 9 , wherein the processing the input domain name includes obtaining a substring of the input domain name that includes: an exact match to the brand name, or an approximate match that matches the brand name within a predetermined threshold measure of distance from the brand name.
11. The system of claim 10 , wherein the obtaining includes: obtaining a pre-processed set of key-value pairs that includes the input domain name as a value of a first pre-processed key-value pair, and the brand name as a value of a second pre-processed key-value pair of the pre-processed set of key-value pairs.
12. The system of claim 10 , wherein the obtaining includes: obtaining a pre-processed set of key-value pairs that includes a substring of the input domain name that is at least an approximate match to the brand name as a value of a first pre-processed key-value pair of the pre-processed set of key-value pairs.
13. The system of claim 12 , wherein the pre-processed set of key-value pairs includes one or more index values indicating a location of the substring of the input domain name that is at least an approximate match to the brand name.
14. The system of claim 12 , wherein the pre-processed set of key-value pairs includes one or more index values indicating a starting location and an ending location of the substring of the input domain name that is at least an approximate match to the brand name.
15. The system of claim 9 , further comprising: obtaining the brand name.
16. The system of claim 9 , wherein the obtaining includes analyzing a plurality of word vectors closest to a word vector for the term of interest.
17. The system of claim 9 , wherein the determining the cluster of least-distant language models includes analyzing a plurality of language models within a predetermined threshold distance from the language model for the term of interest.
18. A computer program product having a non-transitory computer-readable medium storing instructions translatable by the processor for: determining, in pre-processing, terms that are relevant to a brand, the determining including: analyzing words used in domain names registered to the brand; for each term of interest, obtaining a language model and language models that are most similar to the term of interest, the obtaining including: computing a distance between the language model for the term of interest and each of a plurality of language models associated with a set of words; and determining a cluster of least-distant language models from the plurality of language models based on the distance between the language model for the term of interest and a respective language model of the plurality of language models associated with the set of words, the cluster of least-distant language models associated with neighbors of the term of interest; computing a score indicating a relevance of the term of interest to the brand, the computing including determining a number of the neighbors appearing in the domain names registered to the brand; determining, in processing an input domain name, character counts and locations of the characters in the input domain name; for each term indicated by the character counts and the locations of the characters in the input domain name, retrieving a relevance score computed in the pre-processing; determining a final score for the input domain name based at least on the relevance score of each term in the input domain name; and providing the final score to a classifier for classifying the input domain name in relevance to the brand.
19. The computer program product of claim 18 , wherein the processing the input domain name includes obtaining a substring of the input domain name that is at least an approximate match to the brand name.
20. The computer program product of claim 19 , wherein the substring of the input domain name includes: an exact match to the brand name, or an approximate match that matches the brand name within a predetermined threshold measure of distance from the brand name.
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August 28, 2017
June 2, 2020
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